Name
Abstract | ||
|---|---|---|
The paper presents a novel algorithm for object space reconstruction from the planar (2D) recorded data set of a 3D-integral image. The integral imaging system is described and the associated point spread function is given. The space data extraction is formulated as an inverse problem, which proves ill-conditioned, and tackled by imposing additional conditions to the sought solution. An adaptive constrained 3D-reconstruction regularization algorithm based on the use of a sigmoid function is presented. A hierarchical multiresolution strategy which employes the adaptive constrained algorithm to obtain highly accurate intensity maps of the object space is described. The depth map of the object space is extracted from the intensity map using a weighted Durbin–Willshaw algorithm. Finally, illustrative simulation results are given. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1023/A:1023386402756 | VLSI Signal Processing |
Keywords | Field | DocType |
integral imaging,object space reconstruction,inverse problems,regularization methods,gradient descent,Durbin–Willshaw scheme | Integral imaging,Computer vision,Gradient descent,Computer science,Planar,Regularization (mathematics),Artificial intelligence,Inverse problem,Depth map,Point spread function,Sigmoid function | Journal |
Volume | Issue | ISSN |
35 | 1 | 0922-5773 |
Citations | PageRank | References |
5 | 1.46 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Silvia Manolache Cirstea | 1 | 5 | 1.46 |
| S. Y. Kung | 2 | 111 | 16.32 |
| Malcolm McCormick | 3 | 9 | 2.99 |
| Amar Aggoun | 4 | 115 | 21.34 |